Inertial navigation of a platform is based upon the integration of specific forces and angular rates measured by inertial sensors (e.g. accelerometer, gyroscopes) by a device containing the sensors. In general, the device is positioned within the platform and commonly tethered to the platform. Such measurements from the device may be used to determine the position, velocity and attitude of the device and/or the platform.
The platform may be a motion-capable platform that may be temporarily stationary. Some of the examples of the platforms may be a person, a vehicle or a vessel of any type. In the present disclosure the platform is a cycling platform as defined below.
Nowadays, Micro-Electro-Mechanical System (MEMS) based inertial sensors have the advantages of low cost, low power consumption, low weight and small size. Based on these characteristics, the MEMS-inertial sensors can be integrated with other motion sensors (such as barometer, magnetometer) in small devices such as watches, goggles, shoes, belts, smart phones, or custom built devices, etc. Inertial sensors are self-contained systems that are not dependent on the transmission of signals or reception from an external source, thereby minimizing problems like signal blockage, jamming and multipath caused by various environments. When used in navigation, inertial sensors provide high data rate acceleration and angular rate measurements. In addition to navigation, low-cost MEMS sensors are also used in sports to aid sports training. By the advantage of small size, low power consumption and affordable prices, MEMS inertial sensors make it possible to obtain biomechanical, physical or cognitive information from monitoring the user's performance during sport practices. However, the main draw-back of MEMS-based inertial sensors in all the aforementioned applications is that performance of traditional navigation solutions relying on these sensors can deteriorate over time. Therefore, it is important to employ independent measurements as updated measurements for decreasing accumulated errors because these MEMS-based sensors have very low quality performance for navigation purposes due to the sensors' large errors. Therefore, MEMS inertial sensors cannot work alone for long term navigation uses and require assistance from other sensors (such as for example magnetometer and barometer) as well as aid from other reference based systems that can provide absolute navigational information.
One such source of absolute navigation information is the Global Navigation Satellite System (GNSS), which is a positioning system that calculates a user's position and velocity by means of trilateration techniques. In other words, GNSS estimates the user position by knowing the satellites' current location and corresponding distances to the object. When in open sky, GNSS can provide relatively accurate performance in position and velocity. However, it has several disadvantages that limit its implementation in the environments that do not have clear line of sight and can suffer from signal degradation or complete blockage.
In order to solve the problem mentioned above, GNSS has been integrated with Inertial Navigation System (INS). GNSS/INS integration system have been widely used in various applications. However, when GNSS is degraded or blocked, MEMS-based sensors have to work alone and the traditional positioning solution will degrade in a short duration. The system errors in MEMS-based accelerometer and gyroscope grow quickly with the mathematical integration operations and result in an accumulation of errors.
In addition to the above discussed problems that are common for all commercial INS/GNSS applications and that need particular procedures for each type of application, there are additional problems which may affect the application at hand. Generally speaking, alignment of the inertial sensors within the platform (and with the platform's forward, transversal and vertical axis) is critical for inertial navigation. If the inertial sensors, such as accelerometers and gyroscopes are not exactly aligned with the platform, the positions and attitude calculated using the readings of the inertial sensors will not be representative of the platform. Fixing the inertial sensors within the platform is thus a traditional requirement for navigation systems that provide high accuracy navigation solutions.
For tethered systems, one known means for ensuring optimal navigation solutions is to utilize careful manual mounting of the inertial sensors within the platform. However, portable navigation devices (or navigation-capable devices) are able to move whether constrained or unconstrained within the platform (such as for example on the body of the person cycling), so careful mounting on the platform is very difficult.
As such, there is a need for a method and apparatus for cycling applications to provide an enhanced navigation solution capable of accurately utilizing measurements from a device to determine the navigation state of the device/platform while decreasing the effect of the above mentioned problems, without any constraints on the environments where cycling happens (i.e. in outdoor, indoor, in urban canyons, or tunnels, among other environments), and also without implying harsh constraints on the device. The estimation of the position and attitude of the platform has to be independent of the location of the device (such as, for example, the device can be on back, chest, leg, arm, thigh, belt, or pocket of the person cycling).
In addition to applications that include a full navigation solution including position, velocity and attitude, or position and attitude, there are other applications that may include estimating an attitude only solution, an attitude and velocity solution, or the distance traveled solution whether alone or combined with any other estimated quantity. In all these applications, there is a need for a method and apparatus to enhance the determination of such quantities for enhancing the user experience, usability, coaching, and performance analysis for cycling applications.